# A Segmented Robot Grasping Perception Neural Network for Edge AI

arXiv: https://arxiv.org/abs/2507.13970 · HTML: https://mlsanity.com/paper/2507.13970/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference](https://mlsanity.com/paper/2102.04503/) (2021)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [Fully Convolutional Grasp Detection Network with Oriented Anchor Box](https://mlsanity.com/paper/1803.02209/) (2018)
- [An Economic Framework for 6-DoF Grasp Detection](https://mlsanity.com/paper/2407.08366/) (2024)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks](https://mlsanity.com/paper/1612.03940/) (2016)
- [Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2403.18546/) (2024)
- [Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction](https://mlsanity.com/paper/2305.09510/) (2023)
- [6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling](https://mlsanity.com/paper/2407.01209/) (2024)

## Built on by (most cited first, 1 of 1)

- [Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs](https://mlsanity.com/paper/2509.11480/) (2025)

## Abstract (arXiv)

Robotic grasping, the ability of robots to reliably secure and manipulate objects of varying shapes, sizes and orientations, is a complex task that requires precise perception and control. Deep neural networks have shown remarkable success in grasp synthesis by learning rich and abstract representations of objects. When deployed at the edge, these models can enable low-latency, low-power inference, making real-time grasping feasible in resource-constrained environments. This work implements Heatmap-Guided Grasp Detection, an end-to-end framework for the detection of 6-Dof grasp poses, on the GAP9 RISC-V System-on-Chip. The model is optimised using hardware-aware techniques, including input dimensionality reduction, model partitioning, and quantisation. Experimental evaluation on the GraspNet-1Billion benchmark validates the feasibility of fully on-chip inference, highlighting the potential of low-power MCUs for real-time, autonomous manipulation.